Our PhD and MS level biostatisticians are highly trained in a range of statistical and analytic methods, including:
- Longitudinal data analysis
- ANOVA, regression, logistic regression
- Bayesian data analyses
- Sample size and power estimation
- Statistical genomics
- Survival analyses
- Principal component and factor analysis
- Path modeling
- Structural equation modeling
- Cluster analysis
- Complex survey data analysis
- Statistical simulations and graphics
- Profile analysis
- Gene expression data analysis
- Mixed effects models
- Generalized Estimating Equations (GEE)
- Propensity Score Matching (PSM)
- Evaluation of medical tests for classification and prediction
- Estimation of median lethal doses (LD50)/quantal dose-response curves
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Biostatisticians & Epidemiologists
Vikki G. Nolan, DSc, MPH Assistant Professor of Epidemiology, University of Memphis She received her MPH from Yale University and her doctoral degree from Boston University School of Public Health. Dr. Nolan’s primary research interests are sickle cell disease, a field she worked in for six years while at Boston University, and cancer epidemiology, which she specialized in while employed at St. Jude Children’s Research Hospital prior to joining the University of Memphis faculty. In addition to her primary research interests, she interested in and has participated in research around genetics, physical fitness, aging, community health, and infectious diseases.
Mehmet Kocak, PhD Associate Professor of Biostatistics, Preventive Medicine Mehmet earned his M.Sc. degree in applied statistics from Michigan State University and a Ph.D. of statistics from University of Memphis. He has been a study biostatistician for numerous Phase-I and Phase-II clinical trials conducted by St. Jude Children’s Research Hospital from 2002-2011 and by Pediatric Brain Tumor Consortium (PBTC) from 2002-present, and for clinical and observational studies conducted by University of Tennessee Health Science Center (UTHSC) since 2011. His areas of research have been time-course gene expression data analysis, meta-analysis of p-values, Phase-I clinical trial design, Survival analysis, and categorical data analysis. He is an expert in the SAS programming language as well as SAS/Graph.